





'''simple docstring'''

import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation

class    UpperCAmelCase			(    unittest.TestCase	):




		def 					UpperCAmelCase__ (self   :						int					)       ->			Dict:
						lowercase    =			get_activation("swish"					)

						self.assertIsInstance(UpperCamelCase__				,      nn.SiLU					)

						self.assertEqual(act(torch.tensor(-1_0_0				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertNotEqual(act(torch.tensor(-1				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertEqual(act(torch.tensor(0				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertEqual(act(torch.tensor(2_0				,      dtype=torch.floataa					)					).item()				,      2_0					)




		def 					UpperCAmelCase__ (self   :						Optional[int]					)       ->			Tuple:
						lowercase    =			get_activation("silu"					)

						self.assertIsInstance(UpperCamelCase__				,      nn.SiLU					)

						self.assertEqual(act(torch.tensor(-1_0_0				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertNotEqual(act(torch.tensor(-1				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertEqual(act(torch.tensor(0				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertEqual(act(torch.tensor(2_0				,      dtype=torch.floataa					)					).item()				,      2_0					)




		def 					UpperCAmelCase__ (self   :						Tuple					)       ->			int:
						lowercase    =			get_activation("mish"					)

						self.assertIsInstance(UpperCamelCase__				,      nn.Mish					)

						self.assertEqual(act(torch.tensor(-2_0_0				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertNotEqual(act(torch.tensor(-1				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertEqual(act(torch.tensor(0				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertEqual(act(torch.tensor(2_0				,      dtype=torch.floataa					)					).item()				,      2_0					)





		def 					UpperCAmelCase__ (self   :						Union[str, Any]					)       ->			List[Any]:
						lowercase    =			get_activation("gelu"					)

						self.assertIsInstance(UpperCamelCase__				,      nn.GELU					)

						self.assertEqual(act(torch.tensor(-1_0_0				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertNotEqual(act(torch.tensor(-1				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertEqual(act(torch.tensor(0				,      dtype=torch.floataa					)					).item()				,      0					)
						self.assertEqual(act(torch.tensor(2_0				,      dtype=torch.floataa					)					).item()				,      2_0					)












'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase        =							logging.get_logger("transformers.models.speecht5")


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  Dict   ,       lowercase_       :  Any   ):
       """simple docstring"""




       hf_model.apply_weight_norm()

       a_						     =			checkpoint['input_conv.weight_g']
       a_						     =			checkpoint['input_conv.weight_v']
       a_						     =			checkpoint['input_conv.bias']

       for i in range(len(config.upsample_rates   )   ):
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_g']
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_v']
              a_						     =			checkpoint[F'upsamples.{i}.1.bias']

       for i in range(len(config.upsample_rates   ) * len(config.resblock_kernel_sizes   )   ):
              for j in range(len(config.resblock_dilation_sizes   )   ):
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

       a_						     =			checkpoint['output_conv.1.weight_g']
       a_						     =			checkpoint['output_conv.1.weight_v']
       a_						     =			checkpoint['output_conv.1.bias']

       hf_model.remove_weight_norm()


@torch.no_grad()
def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  int   ,       lowercase_       :  Optional[Any]   ,       lowercase_       :  List[str]=None   ,       lowercase_       :  Tuple=None   ,       ):
       """simple docstring"""




       if config_path is not None:
              a_						     =			SpeechTaHifiGanConfig.from_pretrained(lowercase_   )
       else:
              a_						     =			SpeechTaHifiGanConfig()

       a_						     =			SpeechTaHifiGan(lowercase_   )

       a_						     =			torch.load(lowercase_   )
       load_weights(orig_checkpoint['model']['generator']   ,       lowercase_   ,       lowercase_   )

       a_						     =			np.load(lowercase_   )
       a_						     =			stats[0].reshape(-1   )
       a_						     =			stats[1].reshape(-1   )
       a_						     =			torch.from_numpy(lowercase_   ).float()
       a_						     =			torch.from_numpy(lowercase_   ).float()

       model.save_pretrained(lowercase_   )

       if repo_id:
              print('Pushing to the hub...'   )
              model.push_to_hub(lowercase_   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
  parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )

  __lowerCAmelCase        =							parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )




